WeMM-Embedding-4B
WeMM-Embedding-4B is built on Qwen3.5-4B. It maps text, images, visual documents, videos and mixed inputs into 2,560-dimensional normalized vectors.
Model design and access
The supplied code can encode each modality alone or combine items in one input. Query and document methods are provided through Sentence Transformers. The output is an embedding for retrieval or similarity, rather than a generated answer.WeMM-Embedding-4B official model documentation
Practical use
Practical guidance. Compare retrieval on the same query and document set as the smaller model. A larger embedding vector increases index storage, so measure relevance gains before rebuilding an index.
Specifications & access
| Detail | Value and conditions |
|---|---|
| Access | Downloadable |
| License | Apache 2.0; third-party components retain their licensesWeMM-Embedding-4B official model documentation |
| Audio | Not supportedWeMM-Embedding-4B official model documentation |
| Vector output | 2,560 dimensions; L2-normalizedWeMM-Embedding-4B official model documentation |
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